More concretely, we model the scenario that a user's actions affect what content is going to be available to them in the future. If they engage with or otherwise give positive feedback on a piece of content, some content creators are more likely to create more of this type of content in the future, while some others are impervious or less likely to change their behavior ([1]}). In addition, users' actions can also create positive externalities ([2]}) for each other, ie one user can create value for other users: if user \(a\)  likes a piece of content producer by producer \(b\) , which encourages producer \(b\)  to create more content, then if a third user \(c\)  is eligible to see all content that \(b\)  produces, user \(c\)  will also benefit from user \(a\) 's actions. In this paper we work out the solution to how to maximize user value in our recommender system through taking the content production encouragement and user-to-user spillover mechanisms into account, and thereby creating the maximal user value for each user in the ecosystem.
